Build AI Apps with Python: Prompt Engineering Patterns — Few-Shot Chain-of-Thought Role | Episode 24
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Taught by Celeste AI - AI Coding Coach
View on YouTubeDescription
The same question with a better prompt gets a better answer. Four patterns that improve every AI response.
We explore four prompt engineering patterns side by side. Zero-shot vs few-shot — give examples and Claude follows the format. Direct
answer vs chain-of-thought — step-by-step reasoning produces better results. Generic assistant vs role prompting — a teacher uses
analogies, an expert uses jargon. Unformatted vs format control — numbered lists with no fluff. Each pattern runs the same question twice
to show the difference.
Student code: https://github.com/GoCelesteAI/build-ai-apps-python/tree/main/episode24
Every keystroke is shown on screen with 3-second pauses so you can follow along at your own pace.
What You'll Learn:
• Zero-shot vs few-shot prompting
• Chain-of-thought reasoning
• Role prompting for tone and depth
• Output format control
• Side-by-side comparison of each pattern
• A reusable ask() helper function
• When and why to use each pattern
Key Takeaways:
1. Few-shot examples teach the format — show 2-3 examples and Claude follows the pattern
2. Chain-of-thought improves reasoning — "think step by step" forces visible logic and catches errors
3. Role prompting shapes tone — a teacher explains differently than an expert, same content different delivery
This is Episode 24 of Build AI Apps with Python in Neovim — Phase 5 (Real-World AI Patterns).
Taught by CelesteAI. Like & subscribe for more tutorials!
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Tags
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